Run Book Hermes

RunbookHermes is not an ordinary chatbot, nor is it a simple monitoring dashboard. Its goal is to transform the Hermes Agent's Agent Runtime, tool system, memory, skills, gateway, multi-model provider, context compression, and training trajectory capabilities into an AIOps Agent

Realtime Monitoring System Realtime Monitoring System The monitoring page provides a multi-dimensional service health view for payment-service, coupon-service, and order-service, including HTTP status signals, QPS, p95 latency, service topology, backend mode, and deployment.

Product Screenshots The screenshots below show the current RunbookHermes Web Console. Put these images under docs/assets/ and keep the file names consistent with the Markdown paths.

AIOps Console Overview AIOps Console Overview The overview page shows the high-level AIOps control plane: incident count, pending approvals, generated skills, critical services, recommended operation flow, current capability boundaries, and a live monitoring preview.

Digests and Skills  The digest page summarizes recent incidents, high-frequency faults, and generated runbook skills, making RunbookHermes useful for both incident response and operational review.

Integration Readiness and Interface Status  The settings page shows whether model, observability, execution, Feishu, WeCom, and other production integration interfaces are configured. It also documents the environment variables needed to connect real systems.

Self-Evolving Memory Console  The memory console shows how RunbookHermes accumulates operational knowledge over time. It exposes the local memory layer, including Prompt Notebooks, SQLite FTS5 memory search,

RAG Knowledge Base  The RAG knowledge base page allows operators to ingest SRE manuals, service troubleshooting documents, architecture notes, and runbook content into a local citation-based knowledge base.

Benchmark and Evaluation Console  The Benchmark / Eval page measures whether RunbookHermes is improving in the ways that matter for incident response. It evaluates root-cause accuracy, action recommendation accuracy, evidence recall, RAG citation accuracy.

Training / RL / AutoPipeline The Training / RL / AutoPipeline page turns incident handling into model-improvement data. RunbookHermes can convert incidents and benchmark cases into Hermes-compatible trajectories, compressed trajectories, SFT records, preference data.

Multimodal Evidence The multimodal evidence page converts visual or semi-visual operational material into structured incident evidence. It supports inputs such as Grafana screenshots, Feishu alert cards, topology diagrams, log screenshots, OCR text, and monitoring dashboard snap

apps in bankr 🟢 live. x402 endpoints from a prompt 🟢 live. combining the two 🟢 live. open beta. go build.

let me explain what happened here. @Dannyhbrown set up a merch shop for consensus. she's not a dev. with simple prompts to @bankrbot she was able to set up a paid x402 endpoint, public web app, and backend storage system to claim codes for merch.

prompt apps with payments built in. still in beta. klipguy just made a payment gateway for his clipping service.

Today, LienFi officially minted the first tokenized U.S. property tax lien on @base. This marks an important milestone for LienFi and the broader real-world asset ecosystem. We are proud of the work our team has done to bring one of America’s oldest yield markets onchain.

Learn how to build viral social apps on X with @bankrbot Use the X social graph, public leaderboards, and sharing back into the feed. Example: @auraornah Tag “@auraornah check @0xDeployer” to add someone or check their rank. The timeline votes and scores update on a public leaderboard. Start with a simple prompt to @bankrbot, keep prompting on X for live feedback, then move to bankr.bot/terminal if needed. Use Bankr automations to scan the feed and trigger app actions. Use appKV for simple data, or Supabase if you need more. Give it a go and ping us feedback.

aeon skills just got integrated on @bankrbot ⭐ 24 of the best skills created by aeon, available directly on your wallet. narrative tracker → daily map of what's hot in crypto/AI with a clear "front-run / ride / fade / skip" call per narrative. replaces doomscrolling X. autoresearch → auto-upgrades your other skills. generates 4 variants, picks the best, never makes things worse. skill security scan → audits skills before you run them. flags malware, hidden unicode, secret exfil. unlock monitor → tells you which token unlocks will actually dump the price (vs the ones already priced in). heads-up before your bag gets crushed. token pick → one trade idea per run with entry, size, and exit. tells you to skip on flat days instead of forcing a pick. last30 → 30-day sentiment on any topic across reddit/X/HN/polymarket, clustered by narrative, with reddit and HN weighted differently. vuln scanner → "is this trending github repo safe to use." runs real security tools, reports issues privately to the maintainer. and many more.

1/ base trenchers heads up - there’s a fake volume scheme fooling every volume tracker on base. i was building a volume dashboard for base launchpads and one number looked off. dug into it. ended up tracing $69m in fake volume across 178 tokens. here’s how it works.

links: overview + quickstart → docs.bankr.bot/llm-gateway/ov… add credits → bankr.bot/llm full model list → docs.bankr.bot/llm-gateway/mo… bankr skill for agents → docs.bankr.bot/skills/for-oth… 4/4